We use a Hierarchical Attention Network (HAN) [1]} to model both word order and utterance hierarchy. HANs have recently been used to predict emotion in conversations [2]} and are useful for capturing the structure of texts by building up utterance embeddings from word embeddings, and then constructing a conversation vector from these utterance embeddings. Given a sequence of words in an utterance, the words are first mapped to vectors through an embedding matrix (GloVe embeddings, in our case). A bidirectional LSTM layer [3]} is used to process the sequence of embeddings and calculate an embedding for each word in the context of the utterance. An attention mechanism [4]} is applied to these embeddings to find an utterance embedding. A similar procedure is followed to build up a conversation vector based on the utterances embeddings. This vector is then used to perform the classification.
To incorporate edit summaries in a conversation, we prepend each edit summary with a special token (\(<\) EDIT\(>\) ) to indicate its origin.
